op {
  graph_op_name: "LogicalOr"
  endpoint {
    name: "math.logical_or"
  }
  endpoint {
    name: "logical_or"
  }
  description: <<END
Logical OR function.

Requires that `x` and `y` have the same shape or have
[broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
shapes. For example, `x` and `y` can be:

- Two single elements of type `bool`.
- One `tf.Tensor` of type `bool` and one single `bool`, where the result will
  be calculated by applying logical OR with the single element to each
  element in the larger Tensor.
- Two `tf.Tensor` objects of type `bool` of the same shape. In this case,
  the result will be the element-wise logical OR of the two input tensors.

You can also use the `|` operator instead.

Usage:

  >>> a = tf.constant([True])
  >>> b = tf.constant([False])
  >>> tf.math.logical_or(a, b)
  <tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>
  >>> a | b
  <tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>

  >>> c = tf.constant([False])
  >>> x = tf.constant([False, True, True, False])
  >>> tf.math.logical_or(c, x)
  <tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True,  True, False])>
  >>> c | x
  <tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True,  True, False])>

  >>> y = tf.constant([False, False, True, True])
  >>> z = tf.constant([False, True, False, True])
  >>> tf.math.logical_or(y, z)
  <tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, True])>
  >>> y | z
  <tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, True])>

  This op also supports broadcasting

  >>> tf.logical_or([[True, False]], [[True], [False]])
  <tf.Tensor: shape=(2, 2), dtype=bool, numpy=
  array([[ True,  True],
       [ True, False]])>

The reduction version of this elementwise operation is `tf.math.reduce_any`.

Args:
    x: A `tf.Tensor` of type bool.
    y: A `tf.Tensor` of type bool.
    name: A name for the operation (optional).

Returns:
  A `tf.Tensor` of type bool with the shape that `x` and `y` broadcast to.

END
}
